Reference Paper

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting/Autoregressive Denoising Diffusion.bib

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+@misc{rasul2021autoregressivedenoisingdiffusionmodels,
+      title={Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting}, 
+      author={Kashif Rasul and Calvin Seward and Ingmar Schuster and Roland Vollgraf},
+      year={2021},
+      eprint={2101.12072},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2101.12072}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/CSDI Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation/CSDI Conditional Score-based Diffusion Model.bib

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+@misc{tashiro2021csdiconditionalscorebaseddiffusion,
+      title={CSDI Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation}, 
+      author={Yusuke Tashiro and Jiaming Song and Yang Song and Stefano Ermon},
+      year={2021},
+      eprint={2107.03502},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={httpsarxiv.orgabs2107.03502}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Denoising Diffusion Probabilistic Models/Denoising Diffusion Probabilistic Models.bib

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+@inproceedings{NEURIPS2020_4c5bcfec,
+ author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
+ pages = {6840--6851},
+ publisher = {Curran Associates, Inc.},
+ title = {Denoising Diffusion Probabilistic Models},
+ url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
+ volume = {33},
+ year = {2020}
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/DiffSTG Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models/DiffSTG Probabilistic Spatio-Temporal Graph.bib

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+@misc{wen2024diffstgprobabilisticspatiotemporalgraph,
+      title={DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models}, 
+      author={Haomin Wen and Youfang Lin and Yutong Xia and Huaiyu Wan and Qingsong Wen and Roger Zimmermann and Yuxuan Liang},
+      year={2024},
+      eprint={2301.13629},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2301.13629}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/DiffWave A Versatile Diffusion Model for Audio Synthesis/DiffWave A Versatile Diffusion Model for Audio Synthesis.bib

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+@misc{kong2021diffwaveversatilediffusionmodel,
+      title={DiffWave: A Versatile Diffusion Model for Audio Synthesis}, 
+      author={Zhifeng Kong and Wei Ping and Jiaji Huang and Kexin Zhao and Bryan Catanzaro},
+      year={2021},
+      eprint={2009.09761},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2009.09761}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Intro.txt

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+扩散模型（DDPM/Score）用于时间序列/时空建模（最直接支撑你“用 diffusion 生成包序列”）
+
+Ho, Jain, Abbeel. Denoising Diffusion Probabilistic Models (DDPM). NeurIPS 2020.
+用途：扩散模型基本形式（前向加噪、反向去噪、预测噪声训练）。你方法部分的扩散理论根引用。
+
+Song et al. Score-Based Generative Modeling through Stochastic Differential Equations. ICLR 2021.
+用途：score-based diffusion 的更一般表述；如果你未来要做连续时间（时间间隔/抖动）的建模，这条线很有用。
+
+Rasul et al. Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. ICML 2021.
+用途：多变量时间序列的扩散建模；对应你“多个(设备,寄存器)序列”的联合分布生成。
+
+Tashiro et al. CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. NeurIPS 2021.
+用途：条件扩散（conditioning）注入方式很适合你：把设备嵌入/寄存器语义/主从角色/工艺状态作为条件，约束生成。
+
+Liu et al. PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation. ICDE 2023.
+用途：时空条件扩散框架；你把“空间”换成(设备,寄存器)二部图/异构图，“时间”换成轮询/会话位置，结构很贴近。
+
+Wen et al. DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models. ACM SIGSPATIAL 2023.
+用途：扩散 + 时空图；你做(设备,寄存器)图上的生成（而不是预测）时，可借鉴其图特征融入去噪网络的方式。
+
+Kong et al. DiffWave: A Versatile Diffusion Model for Audio Synthesis. ICLR 2021.
+用途：一维信号生成（类似“时间间隔序列”“值序列”）；其 WaveNet/UNet 类去噪骨架对工业轮询类高频序列也很参考。

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/PriSTI A Conditional Diffusion Framework for Spatiotemporal Imputation/PriSTI A Conditional Diffusion Framework for Spatiotemporal .bib

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+@misc{liu2023pristiconditionaldiffusionframework,
+      title={PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation}, 
+      author={Mingzhe Liu and Han Huang and Hao Feng and Leilei Sun and Bowen Du and Yanjie Fu},
+      year={2023},
+      eprint={2302.09746},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2302.09746}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Score-Based Generative Modeling through Stochastic Differential Equations/Score-Based Generative Modeling through Stochastic Differ.bib

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+@misc{song2021scorebasedgenerativemodelingstochastic,
+      title={Score-Based Generative Modeling through Stochastic Differential Equations}, 
+      author={Yang Song and Jascha Sohl-Dickstein and Diederik P. Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
+      year={2021},
+      eprint={2011.13456},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2011.13456}, 
+}